{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ff306b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xie_PQ\\AppData\\Local\\Temp\\ipykernel_14836\\528419022.py:25: RuntimeWarning: divide by zero encountered in divide\n",
      "  betas = model_sm.params / X_sm.std(axis=0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>B (Non-Standardized Coefficients)</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Beta (Standardized Coefficients)</th>\n",
       "      <th>T</th>\n",
       "      <th>P</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>常数</td>\n",
       "      <td>4.3706</td>\n",
       "      <td>0.1483</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.4674</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>性别</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0337</td>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.3875</td>\n",
       "      <td>0.6984</td>\n",
       "      <td>1.2328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>年龄段</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>0.0257</td>\n",
       "      <td>-0.0053</td>\n",
       "      <td>-0.1254</td>\n",
       "      <td>0.9002</td>\n",
       "      <td>1.0821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMI</td>\n",
       "      <td>0.0653</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>0.0844</td>\n",
       "      <td>2.7794</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>1.4449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>手术史</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.2953</td>\n",
       "      <td>0.7678</td>\n",
       "      <td>1.3805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>既往史</td>\n",
       "      <td>-0.0067</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>-0.7068</td>\n",
       "      <td>0.4798</td>\n",
       "      <td>1.4016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>镇静药</td>\n",
       "      <td>0.1330</td>\n",
       "      <td>0.0828</td>\n",
       "      <td>0.2737</td>\n",
       "      <td>1.6073</td>\n",
       "      <td>0.1082</td>\n",
       "      <td>7.0872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>镇静药总剂量</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.2511</td>\n",
       "      <td>0.8018</td>\n",
       "      <td>7.2523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>镇痛药总剂量</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-2.9083</td>\n",
       "      <td>0.0037</td>\n",
       "      <td>2.0755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>术中不良反应严重程度</td>\n",
       "      <td>0.0676</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.1494</td>\n",
       "      <td>1.9677</td>\n",
       "      <td>0.0493</td>\n",
       "      <td>1.0608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>呕吐问题严重程度</td>\n",
       "      <td>-0.2605</td>\n",
       "      <td>0.0446</td>\n",
       "      <td>-0.6621</td>\n",
       "      <td>-5.8372</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.3496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>头部问题严重程度</td>\n",
       "      <td>-0.0902</td>\n",
       "      <td>0.0304</td>\n",
       "      <td>-0.1609</td>\n",
       "      <td>-2.9635</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>1.2764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>精神状态严重程度</td>\n",
       "      <td>-0.0544</td>\n",
       "      <td>0.0304</td>\n",
       "      <td>-0.1019</td>\n",
       "      <td>-1.7879</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>1.1527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>腹部问题严重程度</td>\n",
       "      <td>-0.0419</td>\n",
       "      <td>0.0391</td>\n",
       "      <td>-0.1030</td>\n",
       "      <td>-1.0698</td>\n",
       "      <td>0.2849</td>\n",
       "      <td>1.1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>F-Value</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.9383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>P-Value</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Feature  B (Non-Standardized Coefficients)  Standard Error  \\\n",
       "0           常数                             4.3706          0.1483   \n",
       "1           性别                             0.0130          0.0337   \n",
       "2          年龄段                            -0.0032          0.0257   \n",
       "3          BMI                             0.0653          0.0235   \n",
       "4          手术史                             0.0030          0.0102   \n",
       "5          既往史                            -0.0067          0.0095   \n",
       "6          镇静药                             0.1330          0.0828   \n",
       "7       镇静药总剂量                            -0.0002          0.0008   \n",
       "8       镇痛药总剂量                            -0.0005          0.0002   \n",
       "9   术中不良反应严重程度                             0.0676          0.0344   \n",
       "10    呕吐问题严重程度                            -0.2605          0.0446   \n",
       "11    头部问题严重程度                            -0.0902          0.0304   \n",
       "12    精神状态严重程度                            -0.0544          0.0304   \n",
       "13    腹部问题严重程度                            -0.0419          0.0391   \n",
       "14     F-Value                                NaN             NaN   \n",
       "15     P-Value                                NaN             NaN   \n",
       "\n",
       "    Beta (Standardized Coefficients)        T       P      VIF  \n",
       "0                                NaN  29.4674  0.0000      NaN  \n",
       "1                             0.0262   0.3875  0.6984   1.2328  \n",
       "2                            -0.0053  -0.1254  0.9002   1.0821  \n",
       "3                             0.0844   2.7794  0.0055   1.4449  \n",
       "4                             0.0017   0.2953  0.7678   1.3805  \n",
       "5                            -0.0035  -0.7068  0.4798   1.4016  \n",
       "6                             0.2737   1.6073  0.1082   7.0872  \n",
       "7                            -0.0000  -0.2511  0.8018   7.2523  \n",
       "8                            -0.0000  -2.9083  0.0037   2.0755  \n",
       "9                             0.1494   1.9677  0.0493   1.0608  \n",
       "10                           -0.6621  -5.8372  0.0000   1.3496  \n",
       "11                           -0.1609  -2.9635  0.0031   1.2764  \n",
       "12                           -0.1019  -1.7879  0.0740   1.1527  \n",
       "13                           -0.1030  -1.0698  0.2849   1.1075  \n",
       "14                               NaN      NaN     NaN  11.9383  \n",
       "15                               NaN      NaN     NaN   0.0000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.api import OLS\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from scipy.stats import t\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Load the uploaded file\n",
    "file_path = 'Lasso准备数据.xlsx'\n",
    "data = pd.read_excel(file_path)\n",
    "\n",
    "# Define the independent (X) and dependent (y) variables\n",
    "X = data.iloc[:, 1:15].values\n",
    "y = data.iloc[:, 15].values\n",
    "\n",
    "# Fit a linear regression model using statsmodels\n",
    "X_sm = sm.add_constant(X)  # Add a constant to the independent variables\n",
    "model_sm = sm.OLS(y, X_sm).fit()\n",
    "\n",
    "# Extract coefficients, standard errors, t-values, p-values, and standardized coefficients (beta)\n",
    "coefficients = model_sm.params\n",
    "standard_errors = model_sm.bse\n",
    "t_values = model_sm.tvalues\n",
    "p_values = model_sm.pvalues\n",
    "betas = model_sm.params / X_sm.std(axis=0)\n",
    "\n",
    "# Calculate VIF for each independent variable\n",
    "vif_sm = [variance_inflation_factor(X_sm, i) for i in range(1, X.shape[1] + 1)]\n",
    "\n",
    "# Extract F and P values for the overall model\n",
    "f_value = model_sm.fvalue\n",
    "f_p_value = model_sm.f_pvalue\n",
    "\n",
    "# Create a DataFrame to hold the results\n",
    "results_data = pd.DataFrame({\n",
    "    'Feature': data.columns[1:15],\n",
    "    'B (Non-Standardized Coefficients)': coefficients[1:],  # Exclude the intercept\n",
    "    'Standard Error': standard_errors[1:],  # Exclude the intercept\n",
    "    'Beta (Standardized Coefficients)': betas[1:],  # Exclude the intercept\n",
    "    'T': t_values[1:],  # Exclude the intercept\n",
    "    'P': p_values[1:],  # Exclude the intercept\n",
    "    'VIF': vif_sm\n",
    "})\n",
    "\n",
    "# 提取截距（常数项）的系数、标准误差、t 值和 p 值\n",
    "intercept_coefficient = model_sm.params[0]\n",
    "intercept_standard_error = model_sm.bse[0]\n",
    "intercept_t_value = model_sm.tvalues[0]\n",
    "intercept_p_value = model_sm.pvalues[0]\n",
    "\n",
    "# Calculate the standardized coefficient (beta) for the intercept\n",
    "# Note: Standardized coefficient for the intercept is generally not meaningful\n",
    "# and is usually not reported. Here we calculate it for completeness.\n",
    "intercept_beta = intercept_coefficient / X_sm[:, 0].std() if X_sm[:, 0].std() != 0 else np.nan\n",
    "\n",
    "# Create a DataFrame to hold the results including the intercept\n",
    "results_data_with_intercept = pd.DataFrame({\n",
    "    'Feature': ['常数'] + data.columns[1:15].tolist(),\n",
    "    'B (Non-Standardized Coefficients)': [intercept_coefficient] + coefficients[1:].tolist(),\n",
    "    'Standard Error': [intercept_standard_error] + standard_errors[1:].tolist(),\n",
    "    'Beta (Standardized Coefficients)': [intercept_beta] + betas[1:].tolist(),\n",
    "    'T': [intercept_t_value] + t_values[1:].tolist(),\n",
    "    'P': [intercept_p_value] + p_values[1:].tolist(),\n",
    "    'VIF': [np.nan] + vif_sm  # VIF is not applicable for the intercept\n",
    "})\n",
    "\n",
    "# Add overall model F and P values\n",
    "results_data_with_intercept.loc[len(results_data)] = ['F-Value', np.nan, np.nan, np.nan, np.nan, np.nan, f_value]\n",
    "results_data_with_intercept.loc[len(results_data)+1] = ['P-Value', np.nan, np.nan, np.nan, np.nan, np.nan, f_p_value]\n",
    "\n",
    "# Round the results to four decimal places\n",
    "results_data_with_intercept = results_data_with_intercept.round(4)\n",
    "\n",
    "results_data_with_intercept\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6baa9f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OLS_Test_Results.xlsx'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保存到Excel\n",
    "output_file_path = 'OLS_Test_Results.xlsx'\n",
    "results_data_with_intercept.to_excel(output_file_path, index=False)\n",
    "output_file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87fb3f3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(   性别  年龄段  BMI  手术史  既往史  镇静药  镇静药总剂量  镇痛药总剂量  术中不良反应严重程度  呕吐问题严重程度  \\\n",
       " 0   1    3    1    0    0    0   75.00     350           0         0   \n",
       " 1   1    3    2    0    0    0  105.00     490           0         1   \n",
       " 2   0    2    3    0    0    1   20.45     581           2         0   \n",
       " 3   1    3    2    0    2    1    9.00     420           0         2   \n",
       " 4   1    2    1    0    2    1    9.00     420           0         0   \n",
       " \n",
       "    头部问题严重程度  精神状态严重程度  腹部问题严重程度  其他不良反应严重程度  \n",
       " 0         0         0         0           0  \n",
       " 1         0         0         0           0  \n",
       " 2         1         0         0           0  \n",
       " 3         1         0         0           0  \n",
       " 4         1         0         0           0  ,\n",
       " 0    5\n",
       " 1    4\n",
       " 2    4\n",
       " 3    3\n",
       " 4    4\n",
       " Name: 术后24内患者的满意度评价, dtype: int64)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the uploaded Excel file\n",
    "file_path = 'Lasso准备数据.xlsx'\n",
    "data = pd.read_excel(file_path)\n",
    "\n",
    "# Select columns 2 to 15 as X and column 16 as y\n",
    "X = data.iloc[:, 1:15]\n",
    "y = data.iloc[:, 15]\n",
    "\n",
    "# Display the first few rows of X and y to verify the data\n",
    "X.head(), y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50964bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LassoLarsIC\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the data\n",
    "file_path = 'Lasso准备数据.xlsx'\n",
    "data = pd.read_excel(file_path)\n",
    "\n",
    "# Select columns 2 to 15 as X and column 16 as y\n",
    "X = data.iloc[:, 1:15]\n",
    "y = data.iloc[:, 15]\n",
    "\n",
    "# Fit Lasso model with AIC criterion\n",
    "lasso_lars_ic_aic = make_pipeline(StandardScaler(), LassoLarsIC(criterion=\"aic\")).fit(X, y)\n",
    "\n",
    "# Fit Lasso model with BIC criterion\n",
    "lasso_lars_ic_bic = make_pipeline(StandardScaler(), LassoLarsIC(criterion=\"bic\")).fit(X, y)\n",
    "\n",
    "# Retrieve the alpha values and criteria for AIC and BIC\n",
    "alpha_values = lasso_lars_ic_aic[-1].alphas_\n",
    "aic_criteria = lasso_lars_ic_aic[-1].criterion_\n",
    "bic_criteria = lasso_lars_ic_bic[-1].criterion_\n",
    "\n",
    "# Create a DataFrame to store the results\n",
    "results = pd.DataFrame({\n",
    "    \"alphas\": alpha_values,\n",
    "    \"AIC criterion\": aic_criteria,\n",
    "    \"BIC criterion\": bic_criteria\n",
    "}).set_index(\"alphas\")\n",
    "\n",
    "# Plot the results\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot AIC and BIC criteria\n",
    "ax.plot(results.index, results[\"AIC criterion\"], label=\"AIC criterion\", color=\"tab:blue\")\n",
    "ax.plot(results.index, results[\"BIC criterion\"], label=\"BIC criterion\", color=\"tab:orange\")\n",
    "\n",
    "# Highlight the alpha value that minimizes the AIC and BIC criterion\n",
    "aic_min = results[\"AIC criterion\"].idxmin()\n",
    "bic_min = results[\"BIC criterion\"].idxmin()\n",
    "ax.axvline(x=aic_min, linestyle='--', color='tab:blue', label=f'alpha: AIC estimate ({aic_min:.4f})')\n",
    "ax.axvline(x=bic_min, linestyle='--', color='tab:orange', label=f'alpha: BIC estimate ({bic_min:.4f})')\n",
    "\n",
    "# Set plot labels and title\n",
    "ax.set_xlabel(r\"$\\alpha$\")\n",
    "ax.set_ylabel(\"criterion\")\n",
    "ax.set_xscale(\"log\")\n",
    "ax.legend()\n",
    "ax.set_title(\n",
    "    f\"Information-criterion for model selection\"\n",
    ")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94216137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoLarsCV\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "\n",
    "# Load the data\n",
    "file_path = 'Lasso准备数据.xlsx'\n",
    "data = pd.read_excel(file_path)\n",
    "\n",
    "# Select columns 2 to 15 as X and column 16 as y\n",
    "X = data.iloc[:, 1:15]\n",
    "y = data.iloc[:, 15]\n",
    "\n",
    "model = make_pipeline(StandardScaler(), LassoLarsCV(cv=10)).fit(X, y)\n",
    "\n",
    "lasso = model[-1]\n",
    "plt.semilogx(lasso.cv_alphas_, lasso.mse_path_, \":\")\n",
    "plt.semilogx(\n",
    "    lasso.cv_alphas_,\n",
    "    lasso.mse_path_.mean(axis=-1),\n",
    "    color=\"black\",\n",
    "    label=\"Average across the folds\",\n",
    "    linewidth=2,\n",
    ")\n",
    "plt.axvline(lasso.alpha_, linestyle=\"--\", color=\"black\", label=f\"alpha: {lasso.alpha_:.4f}\")\n",
    "\n",
    "plt.xlabel(r\"$\\alpha$\")\n",
    "plt.ylabel(\"Mean square error\")\n",
    "plt.legend()\n",
    "_ = plt.title(f\"Mean square error on each fold: Lars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "008047ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing regularization path using the lasso...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-1.576216869701127,\n",
       " 5.354916125529252,\n",
       " -0.16286115586164573,\n",
       " 0.904676512128787)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import lasso_path\n",
    "import pandas as pd\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号\n",
    "plt.rcParams['font.size'] = 15 # Increase the font size\n",
    "plt.rcParams['axes.labelweight'] = 'bold' # Make the labels bold\n",
    "\n",
    "# Load the data\n",
    "file_path = 'Lasso准备数据.xlsx'\n",
    "data = pd.read_excel(file_path)\n",
    "\n",
    "# Select columns 2 to 15 as X and column 16 as y\n",
    "X = data.iloc[:, 1:15]\n",
    "y = data.iloc[:, 15]\n",
    "\n",
    "\n",
    "\n",
    "X /= X.std(axis=0)  # Standardize data (easier to set the l1_ratio parameter)\n",
    "\n",
    "# Compute paths\n",
    "eps = 5e-7  # the smaller it is the longer is the path\n",
    "\n",
    "print(\"Computing regularization path using the lasso...\")\n",
    "alphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)\n",
    "\n",
    "\n",
    "# Display results\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "colors = cycle([\"b\", \"r\", \"g\", \"c\", \"#20B2AA\",\"m\",\"y\",\"#FFA500\",\"#9370DB\",\"#FFD700\",\"#FF69B4\",\"#8A2BE2\",\"#00FF7F\"])\n",
    "neg_log_alphas_lasso = -np.log10(alphas_lasso)\n",
    "for coef_l, c in zip(coefs_lasso, colors):\n",
    "    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n",
    "\n",
    "\n",
    "# Remove the top and right spines\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# plt.legend(X.columns, loc=\"upper left\",prop={'size': 10})\n",
    "plt.xlabel(\"-Log(alpha)\")\n",
    "plt.ylabel(\"coefficients\")\n",
    "plt.title(\"Lasso Paths\")\n",
    "plt.axis(\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48914ead",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d027f014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>受试者编号</th>\n",
       "      <th>性别</th>\n",
       "      <th>年龄段</th>\n",
       "      <th>BMI</th>\n",
       "      <th>手术史</th>\n",
       "      <th>既往史</th>\n",
       "      <th>镇静药</th>\n",
       "      <th>镇静药总剂量</th>\n",
       "      <th>镇痛药总剂量</th>\n",
       "      <th>术中不良反应严重程度</th>\n",
       "      <th>呕吐问题严重程度</th>\n",
       "      <th>头部问题严重程度</th>\n",
       "      <th>精神状态严重程度</th>\n",
       "      <th>腹部问题严重程度</th>\n",
       "      <th>其他不良反应严重程度</th>\n",
       "      <th>术后24内患者的满意度评价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.00</td>\n",
       "      <td>350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>105.00</td>\n",
       "      <td>490</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20.45</td>\n",
       "      <td>581</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9.00</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9.00</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   受试者编号  性别  年龄段  BMI  手术史  既往史  镇静药  镇静药总剂量  镇痛药总剂量  术中不良反应严重程度  呕吐问题严重程度  \\\n",
       "0      1   1    3    1    0    0    0   75.00     350           0         0   \n",
       "1      2   1    3    2    0    0    0  105.00     490           0         1   \n",
       "2      3   0    2    3    0    0    1   20.45     581           2         0   \n",
       "3      4   1    3    2    0    2    1    9.00     420           0         2   \n",
       "4      5   1    2    1    0    2    1    9.00     420           0         0   \n",
       "\n",
       "   头部问题严重程度  精神状态严重程度  腹部问题严重程度  其他不良反应严重程度  术后24内患者的满意度评价  \n",
       "0         0         0         0           0              5  \n",
       "1         0         0         0           0              4  \n",
       "2         1         0         0           0              4  \n",
       "3         1         0         0           0              3  \n",
       "4         1         0         0           0              4  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the uploaded Excel file\n",
    "file_path = 'Lasso准备数据.xlsx'\n",
    "data = pd.read_excel(file_path)\n",
    "\n",
    "# Display the first few rows of the dataframe to understand its structure\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fdbd12c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最小均方误差 ( MSE):0.28666048684983253\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4.539475596692894,\n",
       " array(['BMI', '镇静药总剂量', '镇痛药总剂量', '术中不良反应严重程度', '呕吐问题严重程度', '头部问题严重程度',\n",
       "        '精神状态严重程度'], dtype=object),\n",
       " array([ 0.06027946, -0.00131961, -0.00058738,  0.00978738, -0.20057806,\n",
       "        -0.0691432 , -0.02584486]),\n",
       " 'y = 4.539475596692894 + 0.06027946248409606 * BMI+-0.0013196067310506246 * 镇静药总剂量+-0.0005873792988392451 * 镇痛药总剂量+0.009787380608568652 * 术中不良反应严重程度+-0.20057805678915352 * 呕吐问题严重程度+-0.06914319656635128 * 头部问题严重程度+-0.025844856904410553 * 精神状态严重程度')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import Lasso\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "# Separate the features (X) and the target variable (y)\n",
    "X = data.iloc[:, 1:15]\n",
    "y = data.iloc[:, 15]\n",
    "\n",
    "# Split the dataset into training and test sets (80% training, 20% test)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = lasso_with_intercept.predict(X_test)\n",
    "\n",
    "# Calculate the Mean Squared Error (MSE)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"最小均方误差 ( MSE):{mse}\")\n",
    "\n",
    "lasso_with_intercept = Lasso(alpha=0.017, fit_intercept=True)\n",
    "\n",
    "# Fit the model with the constant term to the training data\n",
    "lasso_with_intercept.fit(X_train, y_train)\n",
    "\n",
    "# Get the intercept and coefficients of the fitted model\n",
    "intercept = lasso_with_intercept.intercept_\n",
    "lasso_coef_with_intercept = lasso_with_intercept.coef_\n",
    "\n",
    "# Filter out the features with non-zero coefficients\n",
    "selected_features_with_intercept = np.array(X.columns)[lasso_coef_with_intercept != 0]\n",
    "selected_coefficients_with_intercept = lasso_coef_with_intercept[lasso_coef_with_intercept != 0]\n",
    "\n",
    "# Create the equation with the intercept\n",
    "equation_with_intercept = f\"y = {intercept} + \" + \"+\".join([f\"{coef} * {feature}\" for coef, feature in zip(selected_coefficients_with_intercept, selected_features_with_intercept)])\n",
    "\n",
    "intercept, selected_features_with_intercept, selected_coefficients_with_intercept, equation_with_intercept\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e5630c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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